financial risk meter

Details

We propose a linear lasso measure to estimate systemic interconnectedness across financial institutions based on tail-driven spill-over effects in an ultra-high dimensional framework. Methodologically, we employ a variable selection technique in a time series setting for a linear quantile regression framework with 5% quantile. We can thus include more financial institutions into the analysis, to measure their interdependencies in tails.

Then FRM is induced from this model which is the averaged tuning parameter lambda from lasso technique. The estimation method of it is cross validation. In application we apply 100 US publicly traded financial institutions and 6 macro state variables to estimate this index. Previously we have used 200 financial institutions, after comparison we find out that using 100 firms is more efficient way.

The recession indicators for the US and Euro Area can be found at the St. Louis Fed and CEPR websites, both using a similar methodology. According to the NBER, a recession is a significant decline in economic activity spread across the economy, lasting more than a few months, normally visible in real GDP, real income, employment, industrial production, and wholesale-retail sales. The recession indicator has a value of 1 in a recessionary period, while a value of 0 indicates an expansionary period.

In the paper we detail how the FRM shows a predictability to an imminent recession and serves therefore as an indicator for systemic risk in a variety of world regions. We, therefore, suggest that FRM can be considered in the inclusion of the list of leading indicators and is informative in terms of predicting upcoming recessions.